Optimal rate allocation for entropy-coded uniform scalar quantization of dependent sources in nonbinary hypothesis testing

Authors:
Ali Tabesh;Michael W. Marcellin;Mark A. Neifeld
Affiliations:
Department of Radiology, New York University School of Medicine, New York, NY and Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering and College of Optical Sciences, The University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering and College of Optical Sciences, The University of Arizona, Tucson, AZ
Venue:
IEEE Transactions on Communications
Year:
2010

Citing 2
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Abstract

We propose a closed-form rate allocation scheme (RAS) for entropy-coded uniform scalar quantization of dependent sources in classification problems. The proposed RAS is applicable to nonbinary classification with piecewise monotonic unquantized Bayes decision boundaries. The RAS is also extended to joint compression and classification.